Abstract
With the increasing availability of aerial and satellite imagery, deep learning presents significant potential for transportation asset management, safety analysis, and urban planning. This study introduces CrosswalkNet, a robust and efficient deep learning framework designed to detect various types of pedestrian crosswalks from 15-cm resolution aerial images. CrosswalkNet incorporates a novel detection approach that improves upon traditional object detection strategies by utilizing oriented bounding boxes (OBB), enhancing detection precision by accurately capturing crosswalks regardless of their orientation. Several optimization techniques, including Convolutional Block Attention, a dual-branch Spatial Pyramid Pooling-Fast module, and cosine annealing, are implemented to maximize performance and efficiency. A comprehensive dataset comprising over 23,000 annotated crosswalk instances is utilized to train and validate the proposed framework. The best-performing model achieves an impressive precision of 96.5% and a recall of 93.3% on aerial imagery from Massachusetts, demonstrating its accuracy and effectiveness. CrosswalkNet has also been successfully applied to datasets from New Hampshire, Virginia, and Maine without transfer learning or fine-tuning, showcasing its robustness and strong generalization capability. Additionally, the crosswalk detection results, processed using High-Performance Computing (HPC) platforms and provided in polygon shapefile format, have been shown to accelerate data processing and detection, supporting real-time analysis for safety and mobility applications. This integration offers policymakers, transportation engineers, and urban planners an effective instrument to enhance pedestrian safety and improve urban mobility.
Abstract (translated)
随着高空和卫星图像的日益普及，深度学习在交通资产管理、安全分析和城市规划方面展现出了巨大的潜力。本文介绍了CrosswalkNet，这是一个旨在从15厘米分辨率的航空影像中检测各种类型人行横道的强大且高效的深度学习框架。 CrosswalkNet引入了一种新的检测方法，通过使用定向边界框（OBB）改进了传统的物体检测策略，从而提高了检测精度，无论人行横道的方向如何都能准确捕捉。为了最大化性能和效率，采用了多种优化技术，包括卷积块注意力、双分支空间金字塔池化快速模块以及余弦退火。 研究团队利用了一个包含超过23,000个人行横道路段注释的全面数据集来训练和验证所提出的框架。最佳模型在马萨诸塞州航空影像上达到了令人印象深刻的96.5%精度和93.3%召回率，证明了其准确性和有效性。 此外，CrosswalkNet无需转移学习或微调，已成功应用于新罕布什尔州、弗吉尼亚州和缅因州的数据集，展示了它的稳健性及强大的泛化能力。人行横道检测结果在使用高性能计算（HPC）平台处理后，以多边形shapefile格式提供，这大大加快了数据处理速度，并支持实时安全性和移动应用分析。 该集成向政策制定者、交通工程师和城市规划师提供了一个有效的工具，用于增强行人安全并改善城市流动性。
URL
https://arxiv.org/abs/2506.07885
